Abstract
The aim of this study is to evaluate the performance of BP neural network techniques in predicting earthquakes occurring in the region of Himalayan belt (with the use of different types of input data). These parameters are extracted from Himalayan Earthquake catalogue comprised of all minor, major events and their aftershock sequences in the Himalayan basin for the past 128 years from 1887 to 2015. This data warehouse contains event data, event time with seconds, latitude, longitude, depth, standard deviation and magnitude. These field data are converted into eight mathematically computed parameters known as seismicity indicators. These seismicity indicators have been used to train the BP Neural Network for better decision making and predicting the magnitude of the pre-defined future time period. These mathematically computed indicators considered are the clustered based on every events above 2.5 magnitude, total number of events from past years to 2014, frequency-magnitude distribution b-values, Gutenberg-Richter inverse power law curve for the n events, the rate of square root of seismic energy released during the n events, energy released from the event, the mean square deviation about the regression line based on the Gutenberg-Richer inverse power law for the n events, coefficient of variation of mean time and average value of the magnitude for last n events. We propose a three-layer feed forward BP neural network model to identify factors, with the actual occurrence of the earthquake magnitude M and other seven mathematically computed parameters seismicity indicators as input and target vectors in Himalayan basin area. We infer through comparing curve as observed from seismometer in Himalayan Earthquake catalogue comprised of all events above magnitude 2.5 mg, their aftershock sequences in the Himalayan basin of year 2015 and BP neural network predicting earthquakes in 2015. The model yields good prediction result for the earthquakes of magnitude between 4.0 and 6.0.
Highlights
Earthquakes (EQ) are one of the most destructive costly natural hazards faced by the nation in which they occur without an explicit warning and may cause serious injuries or loss of human lives as a result of damages and destroy a lot of properties and buildings or other rigid structures
We infer through comparing curve as observed from seismometer in Himalayan Earthquake catalogue comprised of all events above magnitude 2.5 mg, their aftershock sequences in the Himalayan basin of year 2015 and BP neural network predicting earthquakes in 2015
Earthquake prediction refers to the specification of the expected magnitude, geographic location and time of occurrence of a future event with sufficient precision that the ultimate success of a prediction can be evaluated
Summary
Earthquakes (EQ) are one of the most destructive costly natural hazards faced by the nation in which they occur without an explicit warning and may cause serious injuries or loss of human lives as a result of damages and destroy a lot of properties and buildings or other rigid structures. Artificial intelligence, Artificial Neural Network, Fuzzy logic and expert systems have been increasingly used in various applications in the last 30 years: Engineering design, Image recognition [2]; Prediction, Estimation, Pattern recognition, and optimization [3]; Petroleum exploration and production; civil engineering, environmental and water resources engineering, traffic engineering, highway engineering, geotechnical engineering [4]; Image classification [5]; Fingerprint analysis [6]; Software defect prediction [7]; Breast cancer identification [8]; Human action recognition, video surveillance to health-care [9]; Video retrieval [10]; Localization scheme of wireless sensor networks, military surveillance, environmental monitoring, robotics, domestics, animal tracking [11]; Image recognition of plant diseases [12]; Wind power forecasting [13]; Design and analysis of antennas [14]; Image recognition [15]; Multimodal medical image fusion [16]; Satellite data and GPS [17]; water resources engineering [18]; air traffic control [19]; financial forecasting [20]; earthquake prediction [21]-[23]
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